An efficient brain tumor segmentation based on cellular automata and improved tumor-cut algorithm

Over the last few decades, segmentation applied to numerous applications using medical images have rapidly been increased, especially for the big data of magnetic resonance (MR) images. Brain tumor segmentation on MR images is a challenging task in clinical analysis for surgical and treatment planning. Numerous brain tumor segmentation algorithms have been proposed. However, they have still faced the problems of over and under segmentation according to characteristics of ambiguous tumor boundaries. Improving segmentation method is still a challenging research. This paper presents a framework of two paradigms to improve the brain tumor segmentation; image transformation and segmentation algorithm. To cope with ambiguous tumor boundaries, the proposed novel gray-level co-occurrence matrix based cellular automata (GLCM-CA) is presented. GLCM-CA aims to transform an original MR image to the target featured image. It enhances features of the tumor similar to the background areas prior to segmentation. For segmentation, the efficient Tumor-Cut algorithm is improved. Tumor-Cut is an efficient algorithm in tumor segmentation, but faces the problem of robustness in seed growing leading to under segmentation. To cope with this problem, the novel patch weighted distance is proposed in the proposed Improved Tumor-Cut (ITC). ITC significantly enhances the robustness of seed growing. For performance evaluation, BraTS2013 benchmark dataset is empirically experimented throughout in comparison with the state-of-the-art methods using dice quantitative evaluation metrics. Experiments are carried out on 55 real MR images consisting of training and testing datasets. In this regard, the proposed method based on GLCM-CA feature space and ITC provides the outstanding result superior to the state-of-the-art compared methods. (C) 2016 Elsevier Ltd. All rights reserved.